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   "source": [
    "# Week 2 Practice Gradio by Creating Brochure\n",
    "\n",
    "- **Author**: [stoneskin](https://www.github.com/stoneskin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c104f45",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "\n",
    "- Use OpenRouter.ai for all different types of LLM models, include many free models from Google,Meta and Deepseek\n",
    "\n",
    "Full code for the Week2 Gradio practice is below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8d3e1a1-ba54-4907-97c5-30f89a24775b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API key looks good so far\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from typing import List\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import gradio as gr \n",
    "\n",
    "load_dotenv(override=True)\n",
    "\n",
    "api_key = os.getenv('Open_Router_Key')\n",
    "if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
    "   print(\"API key looks good so far\")\n",
    "else:\n",
    "   print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
    "  \n",
    "  \n",
    "openai = OpenAI(\n",
    "    api_key=api_key,\n",
    "    base_url=\"https://openrouter.ai/api/v1\"\n",
    ")\n",
    "\n",
    "MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n",
    "MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n",
    "MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n",
    "MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n",
    "MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n",
    "MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n",
    "MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n",
    "MODEL_OpenAi_o3mini = 'openai/o3-mini'\n",
    "MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n",
    "MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24866034",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL=MODEL_Gemini2Pro  # choice the model you want to use\n",
    "\n",
    "####################\n",
    "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
    "and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
    "Include details of company culture, customers and careers/jobs if you have the information.\"\n",
    "\n",
    "##############################\n",
    "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
    "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
    "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
    "link_system_prompt += \"You should respond in JSON as in this example:\"\n",
    "link_system_prompt += \"\"\"\n",
    "{\n",
    "    \"links\": [\n",
    "        {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
    "        {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "##############################\n",
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "##############################\n",
    "class Website:\n",
    "    \"\"\"\n",
    "    A utility class to represent a Website that we have scraped, now with links\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers)\n",
    "        self.body = response.content\n",
    "        soup = BeautifulSoup(self.body, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        if soup.body:\n",
    "            for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "                irrelevant.decompose()\n",
    "            self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
    "        else:\n",
    "            self.text = \"\"\n",
    "        links = [link.get('href') for link in soup.find_all('a')]\n",
    "        self.links = [link for link in links if link]\n",
    "\n",
    "    def get_contents(self):\n",
    "        return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"\n",
    "    \n",
    "##############################\n",
    "def get_links_user_prompt(website):\n",
    "    user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
    "    user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
    "Do not include Terms of Service, Privacy, email links.\\n\"\n",
    "    user_prompt += \"Links (some might be relative links):\\n\"\n",
    "    user_prompt += \"\\n\".join(website.links)\n",
    "    return user_prompt\n",
    "\n",
    "##############################\n",
    "def get_links(url):\n",
    "    website = Website(url)\n",
    "    response = openai.chat.completions.create(\n",
    "        model=MODEL,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": link_system_prompt},\n",
    "            {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
    "      ],\n",
    "        response_format={\"type\": \"json_object\"}\n",
    "    )\n",
    "    result = response.choices[0].message.content\n",
    "    print(\"get_links:\", result)\n",
    "    return json.loads(result)\n",
    "\n",
    "##############################\n",
    "def get_brochure_user_prompt(company_name, url):\n",
    "    user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
    "    user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
    "    user_prompt += get_all_details(url)\n",
    "    user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
    "    return user_prompt\n",
    "\n",
    "##############################\n",
    "def get_all_details(url):\n",
    "    print(\"get_all_details:\", url)   \n",
    "    result = \"Landing page:\\n\"\n",
    "    result += Website(url).get_contents()\n",
    "    links = get_links(url)\n",
    "    print(\"Found links:\", links)\n",
    "    for link in links[\"links\"]:\n",
    "        result += f\"\\n\\n{link['type']}\\n\"\n",
    "        result += Website(link[\"url\"]).get_contents()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82abe132",
   "metadata": {},
   "outputs": [],
   "source": [
    "########### modified stream brochure function for gradio ###################\n",
    "def stream_brochure(company_name, url):\n",
    "    stream = openai.chat.completions.create(\n",
    "        model=MODEL,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
    "          ],\n",
    "        stream=True\n",
    "    )\n",
    "    \n",
    "\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "902f203b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7872\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7872/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_all_details: https://mlccc.herokuapp.com/\n",
      "get_links: {\n",
      "    \"links\": [\n",
      "        {\"type\": \"about page\", \"url\": \"https://mlccc.herokuapp.com/about.html\"},\n",
      "        {\"type\": \"programs\", \"url\": \"https://mlccc.herokuapp.com/program.html\"},\n",
      "        {\"type\": \"camps\", \"url\": \"https://mlccc.herokuapp.com/camps.html\"},\n",
      "        {\"type\": \"community\", \"url\": \"https://mlccc.herokuapp.com/community.html\"},\n",
      "        {\"type\": \"support\", \"url\": \"https://mlccc.herokuapp.com/support.html\"},\n",
      "        {\"type\": \"press\", \"url\": \"https://mlccc.herokuapp.com/press.html\"},\n",
      "        {\"type\": \"newsletter\", \"url\": \"https://mlccc.herokuapp.com/newsletter.html\"},\n",
      "        {\"type\": \"testimonials\", \"url\": \"https://mlccc.herokuapp.com/testimonial.html\"}\n",
      "    ]\n",
      "}\n",
      "Found links: {'links': [{'type': 'about page', 'url': 'https://mlccc.herokuapp.com/about.html'}, {'type': 'programs', 'url': 'https://mlccc.herokuapp.com/program.html'}, {'type': 'camps', 'url': 'https://mlccc.herokuapp.com/camps.html'}, {'type': 'community', 'url': 'https://mlccc.herokuapp.com/community.html'}, {'type': 'support', 'url': 'https://mlccc.herokuapp.com/support.html'}, {'type': 'press', 'url': 'https://mlccc.herokuapp.com/press.html'}, {'type': 'newsletter', 'url': 'https://mlccc.herokuapp.com/newsletter.html'}, {'type': 'testimonials', 'url': 'https://mlccc.herokuapp.com/testimonial.html'}]}\n"
     ]
    }
   ],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_brochure,\n",
    "    inputs=[gr.Textbox(label=\"company Name\"), gr.Textbox(label=\"URL\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  }
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